Category: Statistics for Machine Learning

In machine learning, a hypothesis is a proposed explanation or solution for a problem. It is a tentative assumption or idea that can be tested and validated using data. In supervised learning, the hypothesis is the model that the algorithm is trained on to make predictions on unseen data. Hypothesis in machine learning is generally expressed…

Bias and variance are two important concepts in machine learning that describe the sources of error in a model’s predictions. Bias refers to the error that results from oversimplifying the underlying relationship between the input features and the output variable. At the same time, variance refers to the error that results from being too sensitive to fluctuations in the…

Skewness and kurtosis are two important measures of the shape of a probability distribution in machine learning. Skewness refers to the degree of asymmetry of a distribution. A distribution is said to be skewed if it is not symmetrical about its mean. Skewness can be positive, indicating that the tail of the distribution is longer…

In machine learning, data distribution refers to the way in which data points are distributed or spread out across a dataset. It is important to understand the distribution of data in a dataset, as it can have a significant impact on the performance of machine learning algorithms. Data distribution can be characterized by several statistical…

Percentiles are a statistical concept used in machine learning to describe the distribution of a dataset. A percentile is a measure that indicates the value below which a given percentage of observations in a group of observations falls. For example, the 25th percentile (also known as the first quartile) is the value below which 25%…

Standard deviation is a measure of the amount of variation or dispersion of a set of data values around their mean. In machine learning, it is an important statistical concept that is used to describe the spread or distribution of a dataset. Standard deviation is calculated as the square root of the variance, which is…

Mean, Median, and Mode are statistical measures used to describe the central tendency of a dataset. In machine learning, these measures are used to understand the distribution of data and identify outliers. Here, we will explore the concepts of Mean, Median, and Mode and their implementation in Python. Mean The “mean” is the average value…

Statistics is a crucial tool in machine learning because it helps us understand the underlying patterns in the data. It provides us with methods to describe, summarize, and analyze data. Let’s see some of the basics of statistics for machine learning. What is Statistics? Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, and…